Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 18, 7217-7235, 2018
https://doi.org/10.5194/acp-18-7217-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
25 May 2018
Large-scale tropospheric transport in the Chemistry–Climate Model Initiative (CCMI) simulations
Clara Orbe1,2,3,a, Huang Yang3, Darryn W. Waugh3, Guang Zeng4, Olaf Morgenstern4, Douglas E. Kinnison5, Jean-Francois Lamarque5, Simone Tilmes5, David A. Plummer6, John F. Scinocca7, Beatrice Josse8, Virginie Marecal8, Patrick Jöckel9, Luke D. Oman10, Susan E. Strahan10,11, Makoto Deushi12, Taichu Y. Tanaka12, Kohei Yoshida12, Hideharu Akiyoshi13, Yousuke Yamashita13,14, Andreas Stenke15, Laura Revell15,16, Timofei Sukhodolov15,17, Eugene Rozanov15,17, Giovanni Pitari18, Daniele Visioni18, Kane A. Stone19,20,b, Robyn Schofield19,20, and Antara Banerjee21 1Goddard Earth Sciences Technology and Research (GESTAR), Columbia, MD, USA
2Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
3Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
4National Institute of Water and Atmospheric Research, Wellington, New Zealand
5National Center for Atmospheric Research (NCAR), Atmospheric Chemistry Observations and Modeling (ACOM) Laboratory, Boulder, USA
6Climate Research Branch, Environment and Climate Change Canada, Montreal, QC, Canada
7Climate Research Branch, Environment and Climate Change Canada, Victoria, BC, Canada
8Centre National de Recherches Météorologiques UMR 3589, Météo-France/CNRS, Toulouse, France
9Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
10Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
11Universities Space Research Association, Columbia, MD, USA
12Meteorological Research Institute (MRI), Tsukuba, Japan
13Climate Modeling and Analysis Section, Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
14Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
15Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
16Bodeker Scientific, Christchurch, New Zealand
17Physikalisch-Meteorologisches Observatorium Davos/World Radiation Centre, Davos, Switzerland
18Department of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, Italy
19School of Earth Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia
20ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, New South Wales 2052, Australia
21Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
anow at: NASA Goddard Institute for Space Studies, New York, NY, USA
bnow at: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, USA
Abstract. Understanding and modeling the large-scale transport of trace gases and aerosols is important for interpreting past (and projecting future) changes in atmospheric composition. Here we show that there are large differences in the global-scale atmospheric transport properties among the models participating in the IGAC SPARC Chemistry–Climate Model Initiative (CCMI). Specifically, we find up to 40 % differences in the transport timescales connecting the Northern Hemisphere (NH) midlatitude surface to the Arctic and to Southern Hemisphere high latitudes, where the mean age ranges between 1.7 and 2.6 years. We show that these differences are related to large differences in vertical transport among the simulations, in particular to differences in parameterized convection over the oceans. While stronger convection over NH midlatitudes is associated with slower transport to the Arctic, stronger convection in the tropics and subtropics is associated with faster interhemispheric transport. We also show that the differences among simulations constrained with fields derived from the same reanalysis products are as large as (and in some cases larger than) the differences among free-running simulations, most likely due to larger differences in parameterized convection. Our results indicate that care must be taken when using simulations constrained with analyzed winds to interpret the influence of meteorology on tropospheric composition.
Citation: Orbe, C., Yang, H., Waugh, D. W., Zeng, G., Morgenstern , O., Kinnison, D. E., Lamarque, J.-F., Tilmes, S., Plummer, D. A., Scinocca, J. F., Josse, B., Marecal, V., Jöckel, P., Oman, L. D., Strahan, S. E., Deushi, M., Tanaka, T. Y., Yoshida, K., Akiyoshi, H., Yamashita, Y., Stenke, A., Revell, L., Sukhodolov, T., Rozanov, E., Pitari, G., Visioni, D., Stone, K. A., Schofield, R., and Banerjee, A.: Large-scale tropospheric transport in the Chemistry–Climate Model Initiative (CCMI) simulations, Atmos. Chem. Phys., 18, 7217-7235, https://doi.org/10.5194/acp-18-7217-2018, 2018.
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Short summary
In this study we compare a few atmospheric transport properties among several numerical models that are used to study the influence of atmospheric chemistry on climate. We show that there are large differences among models in terms of the timescales that connect the Northern Hemisphere midlatitudes, where greenhouse gases and ozone-depleting substances are emitted, to the Southern Hemisphere. Our results may have important implications for how models represent atmospheric composition.
In this study we compare a few atmospheric transport properties among several numerical models...
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